Abstract:
The promise of strong physical unclonable functions (PUF) is to utilize the manufacturing variations of circuit elements to produce an independent and unpredictable response to any input challenge vector. Attacks on PUFs that predict the responses to input challenge vectors offer an interesting research problem. An attacking approach based on the optimization theory and side-channel information is proposed where we estimate the manufacturing variations of the circuit elements and predict the PUF's responses to challenge vectors whose actual responses are not known. We apply this attacking approach on some popular PUF designs, including the Arbiter PUFs, the Memristor Crossbar PUFs, and the XOR Arbiter PUFs. Simulations show a substantial reduction in attack complexity compared with previously proposed machine-learning (ML)-based attacks: we achieve an average reduction of 66% in attack time compared with the ML approach. Despite some overhead, our approach is also applicable when the PUF responses are noisy.